About: Ensemble Kalman filter

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The ensemble Kalman filter (EnKF) is a recursive filter suitable for problems with a large number of variables, such as discretizations of partial differential equations in geophysical models. The EnKF originated as a version of the Kalman filter for large problems (essentially, the covariance matrix is replaced by the sample covariance), and it is now an important data assimilation component of ensemble forecasting. EnKF is related to the particle filter (in this context, a particle is the same thing as an ensemble member) but the EnKF makes the assumption that all probability distributions involved are Gaussian; when it is applicable, it is much more efficient than the particle filter.

Attributes	Values
rdf:type	software yago:WikicatLinearFilters yago:WikicatNonlinearFilters yago:Artifact100021939 yago:Device103183080 yago:Filter103339643 yago:Instrumentality103575240 yago:Object100002684 yago:PhysicalEntity100001930 yago:Whole100003553
rdfs:label	Ensemble Kalman filter (en) Filtre de Kalman d'ensemble (fr) アンサンブルカルマンフィルタ (ja)
rdfs:comment	The ensemble Kalman filter (EnKF) is a recursive filter suitable for problems with a large number of variables, such as discretizations of partial differential equations in geophysical models. The EnKF originated as a version of the Kalman filter for large problems (essentially, the covariance matrix is replaced by the sample covariance), and it is now an important data assimilation component of ensemble forecasting. EnKF is related to the particle filter (in this context, a particle is the same thing as an ensemble member) but the EnKF makes the assumption that all probability distributions involved are Gaussian; when it is applicable, it is much more efficient than the particle filter. (en) アンサンブルカルマンフィルタ（Ensemble Kalman Filter；EnKF）とは、逐次型データ同化手法の一つである。シミュレーションモデル内の状態を表す確率変数について、その分布を実現値集合（アンサンブルと称す）によって保持し、観測を得るごとに、観測モデルをもとにしたカルマンフィルターによる推定により、2次モーメントまでが一致するよう、アンサンブルを修正することを繰り返す方法である。 (ja) Le filtre de Kalman d'ensemble (EnKF) est une variante du filtre de Kalman plus adaptée pour les problèmes de très grande dimension comme les modèles géophysiques, il a fait son apparition en 1994 dans un article de Geir Evensen. L'idée du filtre de Kalman d'ensemble est de représenter la loi recherchée par un échantillon de la variable d'état, et par suite la matrice de covariance du filtre de Kalman devient une matrice de covariance échantillonnée. Le filtre de Kalman d'ensemble est lié au filtre particulaire (les particules représentent la même chose que les membres de l'ensemble). (fr)
dcterms:subject	Monte Carlo methods Nonlinear filters Signal estimation Bayesian statistics Linear filters
Wikipage page ID	9553738 (xsd:integer)
Wikipage revision ID	1093735778 (xsd:integer)
Link from a Wikipage to another Wikipage	Bayesian inference Ensemble forecasting Morphing Numerical weather prediction Particle filter Bayes' theorem Monte Carlo methods Hurricane Vortex Inverse problem Standard deviation Penalty method Correlated Mean Gradient Monte Carlo method Likelihood function Cholesky decomposition Density estimation Squall line Thunderstorm BLAS Nonlinear filters Cauchy distribution Distributed memory ScaLAPACK Data assimilation Signal estimation Normal distribution Partial differential equation Bayes theorem Discretization Kalman filter Probability density function Hat matrix Posterior probability Prior probability Covariance matrix State space representation Bayesian statistics Linear filters LAPACK Tikhonov regularization Recursive filter Positive semidefinite matrix Rank deficient Shared memory architecture Image registration Inverse matrix Recursive Bayesian estimation Observation function Pseudoinverse Sample covariance matrix Singular-value decomposition Statistical independence Random sample Fireline Rain front Sample covariance Sherman–Morrison–Woodbury formula dbr:Coherent_feature dbr:Gaussian_mixture dbr:Localized_ensemble_Kalman_filters dbr:Parallel_Data_Assimilation_Framework
Link from a Wikipage to an external page	http://pdaf.awi.de http://enkf.nersc.no http://topaz.nersc.no https://github.com/sakov/enkf-c
sameAs	Ensemble Kalman filter Ensemble Kalman filter Ensemble Kalman filter Ensemble Kalman filter Ensemble Kalman filter Ensemble Kalman filter
dbp:wikiPageUsesTemplate	dbt:Main dbt:Reflist
has abstract	The ensemble Kalman filter (EnKF) is a recursive filter suitable for problems with a large number of variables, such as discretizations of partial differential equations in geophysical models. The EnKF originated as a version of the Kalman filter for large problems (essentially, the covariance matrix is replaced by the sample covariance), and it is now an important data assimilation component of ensemble forecasting. EnKF is related to the particle filter (in this context, a particle is the same thing as an ensemble member) but the EnKF makes the assumption that all probability distributions involved are Gaussian; when it is applicable, it is much more efficient than the particle filter. (en)

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